Evaluating machine learning approaches for aiding probe selection for gene-expression arrays

Bioinformatics

Volumn 18, Issue SUPPL. 1, 2002, Pages

  Tobler, J B ^a   Molla, M N ^a,b   Nuwaysir, E F ^b   Green, R D ^b   Shavlik, J W ^a  

^a University of Wisconsin Madison   (United States)

^b Roche NimbleGen Inc   (United States)

Author keywords

Artificial neural networks; Decision trees; Microarrays; Na ve Bayes; Probe selection

Indexed keywords

ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BIOLOGICAL MODEL; CONFERENCE PAPER; DNA HYBRIDIZATION; DNA MICROARRAY; DNA PROBE; GENE EXPRESSION PROFILING; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; ARTICLE; COMPARATIVE STUDY; DNA SEQUENCE; EVALUATION; METHODOLOGY; REPRODUCIBILITY; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DNA PROBES; GENE EXPRESSION PROFILING; MODELS, GENETIC; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS, DNA;

EID: 1042275290     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/18.suppl_1.S164     Document Type: Conference Paper

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